data_hope

> he wants mathematics to be about textual rules

that is a princial problem with his blog post.

In a way he is also kind of right - although I don't agree with his conclusion. E[X^2] is not the most precise notation. I have occasionally seen notations like E_X[X^2] to avoid confusion and clearly state "the expected value for X^2 with respect to the distribution of X" or something like that. I remember multiple times were imprecise notation has kept me from understanding statistical methods, while I would say that I am reasonably familiar with the field.

Another problem: Different "schools" use different notations, which are then mixed arbitrarily. This is a major problem for example on Wikipedia, where articles use different notation and sometimes switch notations within the article.

https://github.com/dropbox/pyston seems to be option 3

This can be good advice for some. It might not be choice for someone with depression. Deeper problems are not solved by a vacation.

Another issue: In my experience, in north america, navigation is more focused on compass points (take Road XYZ north, southwest, etc) whereas in Europe it is more focused on semantic directions (turn right on highway towards Groningen). The european way allows for easier verification if the road is actually the road you want to take (as in: do I really want to drive to Hobbington? The street sign says I should take the second exit, so I go with that.

from my experience, gramatical gender is always more difficult than no gener, even when you have a native tongue that employs a grammatical gender. In romance langauges, the moon is feminine ("la lune"), in german it is masculine ("der mond"), so for both french and german speakers learning english ("a moon") should be easier, because it does not require learning an additional bit or two of information.

I would argue that dictionary size does not matter for basic language skills, but becomes more important the closer you get to native proficiency.

and the realities of the job economy is exactly why "not PC"?

of course physical labor does not pay as much, as long as the pool of workers is significantly larger. Doesn't mean that some folks would choose another career path if other fields would pay as much as software engineering does.

not everything has to be binary. In fact outside of math and deterministic CS, we hardly have the comfort of being clearly wrong or right. https://www.cs.rit.edu/~rlaz/prec20092/slides/BayesianDecisi...

uh, this is a very pessimistic view on history rewriting.

Much more common probably is the fact that one tries to persist very gradually local changes, and once the dust has settled, submit a much coarser commit. Because most of the time all these "fixup this", "add missing imports" commits are really of nobodies interest any more.

If you hold on to a git hash, you are can identify history changes very easily.

Original author of the blog post here. I find it interesting that your takeaway from my blog post is that I am writing unfavorable over smalltalk. I quite liked it. I think I mentioned that I was checking out Smalltalk, didn't claim to summarize longer experience with it.

Moose looks interesting indeed.

In my experience, people in science who are not statisticians have not completely understood the p value. Typically people are clueless on p-Values and the statistical power and how they relate.